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  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "direct-premium",
   "metadata": {},
   "source": [
    "# Profiling skforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "312a3228",
   "metadata": {},
   "source": [
    "This document shows the profiling of the main classes, methods and functions available in skforecast. Understanding the bottlenecks will help to:\n",
    "\n",
    "+ Use it more efficiently\n",
    "+ Improve the code for future releases"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "72d13ef0",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "flexible-migration",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import platform\n",
    "import psutil\n",
    "\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "\n",
    "import skforecast\n",
    "from skforecast.recursive import ForecasterRecursive\n",
    "from skforecast.direct import ForecasterDirect\n",
    "from skforecast.model_selection import grid_search_forecaster, backtesting_forecaster\n",
    "\n",
    "%load_ext pyinstrument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98b414fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version      : 3.13.9\n",
      "scikit-learn version: 1.7.2\n",
      "skforecast version  : 0.19.0\n",
      "pandas version      : 2.3.3\n",
      "numpy version       : 2.2.6\n",
      "psutil version      : 7.0.0\n",
      "\n",
      "Machine type: x86_64\n",
      "Processor type: x86_64\n",
      "Platform type: Linux-6.14.0-35-generic-x86_64-with-glibc2.39\n",
      "Operating system: Linux\n",
      "Operating system release: 6.14.0-35-generic\n",
      "Operating system version: #35~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Oct 14 13:55:17 UTC 2\n",
      "Number of physical cores: 4\n",
      "Number of logical cores: 8\n"
     ]
    }
   ],
   "source": [
    "# Versions\n",
    "# ==============================================================================\n",
    "print(f\"Python version      : {platform.python_version()}\")\n",
    "print(f\"scikit-learn version: {sklearn.__version__}\")\n",
    "print(f\"skforecast version  : {skforecast.__version__}\")\n",
    "print(f\"pandas version      : {pd.__version__}\")\n",
    "print(f\"numpy version       : {np.__version__}\")\n",
    "print(f\"psutil version      : {psutil.__version__}\")\n",
    "print(\"\")\n",
    "\n",
    "# System information\n",
    "# ==============================================================================\n",
    "print(f\"Machine type: {platform.machine()}\")\n",
    "print(f\"Processor type: {platform.processor()}\")\n",
    "print(f\"Platform type: {platform.platform()}\")\n",
    "print(f\"Operating system: {platform.system()}\")\n",
    "print(f\"Operating system release: {platform.release()}\")\n",
    "print(f\"Operating system version: {platform.version()}\")\n",
    "print(f\"Number of physical cores: {psutil.cpu_count(logical=False)}\")\n",
    "print(f\"Number of logical cores: {psutil.cpu_count(logical=True)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e794afa1",
   "metadata": {},
   "source": [
    "A time series of length 1000 with random values is created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "extreme-injection",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data\n",
    "# ==============================================================================\n",
    "np.random.seed(123)\n",
    "n = 1_000\n",
    "data = pd.Series(data = np.random.normal(size=n))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "916b4e7b",
   "metadata": {},
   "source": [
    "## Dummy estimator"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "342d7f95",
   "metadata": {},
   "source": [
    "To isolate the training process of the estimator from the other parts of the code, a dummy estimator class is created. This dummy estimator has a fit method that does nothing, and a predict method that returns a constant value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95a95fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DummyRegressor(LinearRegression):\n",
    "    \"\"\"\n",
    "    Dummy estimator with dummy fit and predict methods.\n",
    "    \"\"\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        pass\n",
    "\n",
    "    def predict(self, y):\n",
    "        predictions = np.ones(shape = len(y))\n",
    "        return predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "84dfd427",
   "metadata": {},
   "source": [
    "## Profiling fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bff795a1",
   "metadata": {},
   "outputs": [
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       "/   _/                      v5.1.1\n",
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       "Cell [6]\n",
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       "0.008 <module>  /tmp/ipykernel_26803/930535467.py:1\n",
       "`- 0.008 ForecasterRecursive.fit  skforecast/recursive/_forecaster_recursive.py:955\n",
       "      [18 frames hidden]  skforecast, pandas, numpy\n",
       "         0.002 maybe_infer_to_datetimelike  pandas/core/dtypes/cast.py:1166\n",
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   "source": [
    "%%pyinstrument\n",
    "\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = DummyRegressor(),\n",
    "                 lags      = 24\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data)"
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    "Almost all of the time spent by `fit` is required by the `create_train_X_y` method."
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   "id": "ba8d038b-1f7a-4521-b459-ad20bd0b3b3c",
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       "                    pyinstrumentHTMLRenderer.render(document.getElementById(&#x27;app&#x27;), sessionData);\n",
       "                &lt;/script&gt;\n",
       "            &lt;/body&gt;\n",
       "            &lt;/html&gt;\n",
       "        \"\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "\n",
       "  _     ._   __/__   _ _  _  _ _/_   Recorded: 17:40:20  Samples:  66\n",
       " /_//_/// /_\\ / //_// / //_'/ //     Duration: 0.185     CPU time: 0.499\n",
       "/   _/                      v5.1.1\n",
       "\n",
       "Cell [7]\n",
       "\n",
       "0.184 <module>  /tmp/ipykernel_26803/2499331845.py:1\n",
       "`- 0.183 ForecasterRecursive.fit  skforecast/recursive/_forecaster_recursive.py:955\n",
       "      [131 frames hidden]  sklearn, joblib, multiprocessing, <bu...\n",
       "         0.055 sleep  <built-in>\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%pyinstrument\n",
    "\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = HistGradientBoostingRegressor(max_iter=10, random_state=123),\n",
    "                 lags      = 24\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d5f3b7c1",
   "metadata": {},
   "source": [
    "When training a forecaster with a real machine learning estimator, the time spent by `create_train_X_y` is negligible compared to the time needed by the `fit` method of the estimator. Therefore, improving the speed of `create_train_X_y` will not have much impact."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6735cbc4",
   "metadata": {},
   "source": [
    "## Profiling create_train_X_y"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b4e33347",
   "metadata": {},
   "source": [
    "Understand how the `create_train_X_y` method is influenced by the length of the series and the number of lags. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "551ffdfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>50</th>\n",
       "      <th>100</th>\n",
       "      <th>200</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.000881</td>\n",
       "      <td>0.001552</td>\n",
       "      <td>0.011466</td>\n",
       "      <td>0.010749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250750</th>\n",
       "      <td>0.012573</td>\n",
       "      <td>0.003624</td>\n",
       "      <td>0.027695</td>\n",
       "      <td>0.040414</td>\n",
       "      <td>0.077589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500500</th>\n",
       "      <td>0.018805</td>\n",
       "      <td>0.016675</td>\n",
       "      <td>0.061571</td>\n",
       "      <td>0.095223</td>\n",
       "      <td>0.188530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750250</th>\n",
       "      <td>0.017451</td>\n",
       "      <td>0.021511</td>\n",
       "      <td>0.097587</td>\n",
       "      <td>0.427495</td>\n",
       "      <td>1.073105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000000</th>\n",
       "      <td>0.016540</td>\n",
       "      <td>0.026660</td>\n",
       "      <td>0.608907</td>\n",
       "      <td>0.585123</td>\n",
       "      <td>2.078700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              5         10        50        100       200\n",
       "1000     0.001394  0.000881  0.001552  0.011466  0.010749\n",
       "250750   0.012573  0.003624  0.027695  0.040414  0.077589\n",
       "500500   0.018805  0.016675  0.061571  0.095223  0.188530\n",
       "750250   0.017451  0.021511  0.097587  0.427495  1.073105\n",
       "1000000  0.016540  0.026660  0.608907  0.585123  2.078700"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Profiling `create_train_X_y` for different length of series and number of lags\n",
    "# ======================================================================================\n",
    "series_length = np.linspace(1000, 1000000, num=5, dtype=int)\n",
    "n_lags = [5, 10, 50, 100, 200]\n",
    "results = {}\n",
    "\n",
    "for lags in n_lags:\n",
    "    execution_time = []\n",
    "    forecaster = ForecasterRecursive(\n",
    "                     estimator = DummyRegressor(),\n",
    "                     lags      = lags\n",
    "                 )\n",
    "\n",
    "    for n in series_length:\n",
    "        y = pd.Series(data = np.random.normal(size=n))\n",
    "        tic = time.perf_counter()\n",
    "        _ = forecaster.create_train_X_y(y=y)\n",
    "        toc = time.perf_counter()\n",
    "        execution_time.append(toc - tic)\n",
    "\n",
    "    results[lags] = execution_time\n",
    "\n",
    "results = pd.DataFrame(\n",
    "              data = results,\n",
    "              index = series_length\n",
    "          )\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "70db0dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NVQaDQdWvX1/9+9//drhlhFKlc0uMBQsWOGwrNjZWAWrOnDkO7dOnT1cRERHKaDSqdu3aqU2bNqk2bdqoXr16Xfd95/r6669VVFSUMhqNyt/fX3Xr1k2tXr3avjwiIkL17du3wHVTU1PVyy+/rOrVq6dcXV1VjRo1VKdOndTkyZOVyWSy9/vqq69U/fr1ldFoVI0aNVJz5sxREyZMUFf/93jo0CHVtWtX5e7urgCH22PEx8erkSNHqrCwMGUwGFRwcLDq0aOH+vzzzwv1PnPFxcWpvn37Km9vb4dbh1zv7yUxMVE9/vjjqkaNGsrLy0tFR0erQ4cOqYiICIcYr3VLjIL+NoYOHaoiIiIKHfezzz6rdDqd+uuvvwpcPmrUKKXT6dT27dsLvc1cI0aMUICaN29egct37typAPXHH384tFssFhUSEqJee+21Iu9TCGfRlJL5J4QQZcNqtRIQEMA999zDF1984exwRAUwduxYvvrqK+Li4q55I9wePXoQGhrKd999Z29btGgRDz/8MEePHiUkJKSswhXipkhNmRCiVGRlZeWrdfr222+5dOlSoadZElVbVlYW//nPfxg0aNB1ZyZ47733+OGHHxxuBfPBBx8watQoSchEhSIjZUKIUrF+/XrGjh3LfffdR/Xq1dm5cydfffUVjRs3ZseOHRVqovObdf78+esW3ru6ujpMJ1SelGTsFovlhhc/eHl5kZGRwZo1a/jxxx9ZtGgRO3fuLNZVuEJUNFLoL4QoFbVr1yYsLIzp06fb5zAcMmQI77//fpVKyABuueWW697Qt1u3bg4ThZcnJRn7qVOnCrwBbl4TJkyge/fuDB48mMDAQKZPny4JmagyZKRMCCFK2aZNm8jMzLzmcn9/f/vk2eVNScaelZXFxo0br9unTp061KlTp0gxClFZSFImhBBCCFEOSKG/EEIIIUQ5UOVqyqxWK2fPnsXb21smqRVCCCFEqVNKkZqaSmho6HXnMq5ySdnZs2ftU4AIIYQQQpSVU6dOUatWrWsur3JJWe7kzadOncLHx8fJ0QghhBCisktJSSEsLMyeg1xLlUvKck9Z+vj4SFImhBBCiDJzo7IpKfQXQgghhCgHJCkTQgghhCgHJCkTQgghhCgHqlxNWWEopTCbzded702Ub3q9HhcXF7ntiRBCiApDkrKrmEwmzp07R0ZGhrNDETfJw8ODkJCQKjfPohBCiIpJkrI8rFYrsbGx6PV6QkNDcXV1lZGWCkgphclk4vz588TGxlK/fv3r3qxPCCGEKA8kKcvDZDJhtVoJCwvDw8PD2eGIm+Du7o7BYODEiROYTCbc3NycHZIQQghxXTJ8UAAZVakc5DgKIYSoSORTSwghhBBVVlpiFqdjEklLzHJ2KHL6UgghhBBV08FNZ1n3n0OgQNOg+yONaHJrqNPikZGyCqJ79+6MGTPG2WHYKaV48sknqVatGpqmsXv37nx95s6di5+fX5nHJoQQQtxIWmKWPSEDUArWf3/IqSNmMlImimXlypXMnTuX9evXU6dOHWrUqOHskIQQQohCO3ck2Z6Q5VJWSE7IxMvfOReHSVJWhVksFjRNK1ZB/NGjRwkJCaFTp06lEJkQQghRerLScti65Gi+dk0HvoHuTojIRk5fFkH37t155plneOGFF6hWrRrBwcFMnDjRvvz48eP5TuUlJSWhaRrr168HYP369WiaxqpVq4iKisLd3Z3bb7+dhIQEVqxYQePGjfHx8eHhhx/OdwNbs9nMqFGj8PX1pUaNGrz++usodSXNz87OZvz48dSsWRNPT0/at29v3y9cOZ24ZMkSmjRpgtFo5OTJkwW+1w0bNtCuXTuMRiMhISG89NJLmM1mAB577DFGjx7NyZMn0TSN2rVrF+r3d/ToUQYMGEBQUBBeXl7ccsstrFmzxqHPuXPn6Nu3L+7u7kRGRjJv3jxq167N1KlTAdtp04kTJxIeHo7RaCQ0NJRnnnmmUPsXQgghcrItLJu5h+TzWRg9XMi9Hammg+6DGzltlAxkpKzIvvnmG8aNG8fWrVvZsmULjz32GLfeeit33HFHkbYzceJEPvnkEzw8PLj//vu5//77MRqNzJs3j7S0NO6++25mzJjBiy++6LDvJ554gr/++ovt27fz5JNPEh4ezvDhwwEYNWoUBw8eZP78+YSGhrJw4UJ69erFvn37qF+/PgAZGRl88MEHfPnll1SvXp3AwMB8sZ05c4Y+ffrw2GOP8e2333Lo0CGGDx+Om5sbEydOZNq0adStW5fPP/+cbdu2odfrC/We09LS6NOnD++++y5Go5Fvv/2Wfv36ERMTQ3h4OABDhgzhwoULrF+/HoPBwLhx40hISLBv46effuLjjz9m/vz5NG3alLi4OPbs2VOk370QQoiqyWKxsvLz/cTHpmD0dOGe59rg6q4nOSET30B3pyZkAKgqJjk5WQEqOTk537LMzEx18OBBlZmZWeC63bp1U507d3Zou+WWW9SLL76olFIqNjZWAWrXrl325YmJiQpQ69atU0optW7dOgWoNWvW2PtMmjRJAero0aP2tqeeekpFR0c77Ltx48bKarXa21588UXVuHFjpZRSJ06cUHq9Xp05c8Yhvh49eqiXX35ZKaXUnDlzFKB27959zd+PUkq98sorqmHDhg77mjlzpvLy8lIWi0UppdTHH3+sIiIirrudOXPmKF9f3+v2adq0qZoxY4ZSSqm///5bAWrbtm325YcPH1aA+vjjj5VSSk2ZMkU1aNBAmUym625XqRsfTyGEEFWH1WJVv369X33y1Fo1e9Q6de5oUpnt+3q5R15y+rKIWrRo4fA6JCTEYSSnONsJCgrCw8ODOnXqOLRdvd0OHTo4TPvUsWNHDh8+jMViYd++fVgsFho0aICXl5f9sWHDBo4evXLe3NXVNd97uNrff/9Nx44dHfZ16623kpaWxunTp4v8XnOlpaUxfvx4GjdujJ+fH15eXvz999/2U6gxMTG4uLjQunVr+zr16tXD39/f/vq+++4jMzOTOnXqMHz4cBYuXGg/rSqEEEIURCnFpp+P8M/WeDSdRvSTzQiu4+vssPJxalI2adIkbrnlFry9vQkMDGTgwIHExMTccL0FCxbQqFEj3NzcaN68Ob/88ksZRGtjMBgcXmuahtVqBa7cQV7lqfPKycm54XY0TbvudgsjLS0NvV7Pjh072L17t/3x999/M23aNHs/d3d3p83nOX78eBYuXMh7773HH3/8we7du2nevDkmk6nQ2wgLCyMmJoZPP/0Ud3d3RowYQdeuXa/5exZCCCF2/XqSPWtOAdBjSCNqNy+fdwxwalK2YcMGRo4cyZ9//snq1avJycnhzjvvJD09/ZrrbN68mYceeognnniCXbt2MXDgQAYOHMj+/fvLMPKCBQQEALZi9VwF3b+ruLZu3erw+s8//6R+/fro9XqioqKwWCwkJCRQr149h0dwcHCR9tO4cWO2bNnikFxu2rQJb29vatWqVez4N23axGOPPcbdd99N8+bNCQ4O5vjx4/blDRs2xGw2s2vXLnvbkSNHSExMdNiOu7s7/fr1Y/r06axfv54tW7awb9++YsclhBCi8vp78zm2LLSdMeo0qB4NO4Q4OaJrc2qh/8qVKx1ez507l8DAQHbs2EHXrl0LXGfatGn06tWL559/HoC3336b1atX88knnzB79uxSj/l63N3d6dChA++//z6RkZEkJCTw2muvldj2T548ybhx43jqqafYuXMnM2bMYMqUKQA0aNCAwYMHM2TIEKZMmUJUVBTnz59n7dq1tGjRgr59+xZ6PyNGjGDq1KmMHj2aUaNGERMTw4QJExg3btxNzSdZv359fv75Z/r164emabz++usOo4GNGjWiZ8+ePPnkk8yaNQuDwcBzzz3nMLo3d+5cLBYL7du3x8PDg//85z+4u7sTERFR7LiEEEJUTsf3XrDdIBaIuiOcqDvCnRzR9ZWrmrLk5GQAqlWrds0+W7ZsoWfPng5t0dHRbNmypcD+2dnZpKSkODxK09dff43ZbKZNmzaMGTOGd955p8S2PWTIEDIzM2nXrh0jR47k2Wef5cknn7QvnzNnDkOGDOG5556jYcOGDBw4kG3bttmvbCysmjVr8ssvv/DXX3/RsmVL/vWvf/HEE0/cdIL50Ucf4e/vT6dOnejXrx/R0dEO9WMA3377LUFBQXTt2pW7776b4cOH4+3tjZub7YoYPz8/vvjiC2699VZatGjBmjVrWLp0KdWrV7+p2IQQQlQu544ms+qL/SirolGHYDreU9fZId2QpvKeo3Iiq9VK//79SUpKYuPGjdfs5+rqyjfffMNDDz1kb/v000958803iY+Pz9d/4sSJvPnmm/nak5OT8fHxcWjLysoiNjaWyMhIexIgnOv06dOEhYWxZs0aevToUaR15XgKIUTVdPFsGgsn7yQ7w0xE8+r0/ldz9HrnjUOlpKTg6+tbYO6RV7m5T9nIkSPZv3//dROy4nj55ZcZN26c/XVKSgphYWElug9Rcn777TfS0tJo3rw5586d44UXXqB27drXPJ0thBBC5JV6KYul0/eQnWEmuI4v0cObOTUhK4pykZSNGjWKZcuW8fvvv9+wkDw4ODjfiFh8fPw1i9mNRiNGo7HEYhWlKycnh1deeYVjx47h7e1Np06d+P777/NdnSqEEEJcLTPNxJJpu0lPyqZaqCd9R7bA4Fq4G5yXB05NypRSjB49moULF7J+/XoiIyNvuE7Hjh1Zu3YtY8aMsbetXr2ajh07lmKkoqxER0cTHR3t7DCEEEJUMKYsM8s+2UtSfAZe/kb6jW6Jm2fF+kLv1KRs5MiRzJs3j8WLF+Pt7U1cXBwAvr6+uLvbJgQdMmQINWvWZNKkSQA8++yzdOvWjSlTptC3b1/mz5/P9u3b+fzzz532PoQQQgjhPBazlVWf7yfhuG36pH7PtHL+lEnF4NSTrLNmzSI5OZnu3bsTEhJif/zwww/2PidPnnS471enTp2YN28en3/+OS1btuTHH39k0aJFNGvWzBlvQQghhBBOpKyKtd/8zcmDl3Bx1XHXqJZUC/F0dljF4vTTlzeyfv36fG333Xcf9913XylEJIQQQoiKQinFph+PcHhbPDqdRq+nmhMcWf6mTyqsinE5ghBCCCHEVXauOsGe32zTJ90+tDERTSv2PSslKRNCCCFEhXNw01n+XHQMgFvvrUfD9kWbUrA8kqRMCCGEEBVK7J7zrL88fVLr6HBa9Szf0ycVliRlQgghhKgwzh5OYtWXB1AKGnUKocPA8j99UmFJUiYKNHHiRDRNc3g0atTI2WEJIYSowi6eSeOXWXux5Fip3aIGtw1uiKZpzg6rxJSLO/qLGzuXnEnshXQia3gS4uteJvts2rQpa9assb92cZE/FyGEEM6RcjGTpdN3k51hJqSuL3cOa4qugkyfVFjyKVvGlFJk5liKtM5PO04zYckBrAp0GrzZvymD2lx/OqqruRv0Rf424eLics3pq4QQQoiykplqYun0PaQnm6gW6kmfERVr+qTCkqSsjGXmWGjyxqpir29V8PriA7y++ECR1jv4VjQerkU73IcPHyY0NBQ3Nzc6duzIpEmTCA+vHMWUQgghKgbb9El7SIrPwLuaG/1Gt6pw0ycVVuUa9xMlpn379sydO5eVK1cya9YsYmNj6dKlC6mpqc4OTQghRBVhMVtZ+dk+Ek6k4uZloN8zLfHyNzo7rFIjI2VlzN2g5+BbhZ9wOy45i54fbcCaZ/IDnQZrxnUj2Lfw83q5G4o2zNu7d2/78xYtWtC+fXsiIiL43//+xxNPPFGkbQkhhBBFlTt90qm/E3Ex6rlrZEv8gyvm9EmFJUlZGdM0rUinEesEeDHpnua88vN+LEqh1zTeu6cZdQK8SjHK/Pz8/GjQoAFHjhwp0/0KIYSoepRSbFxw2D59Uu+nmhEU6ePssEqdJGUVwAO3hNO1QQDHL2RQu4ZHmV19mVdaWhpHjx7l0UcfLfN9CyGEqFp2rDzB3nWnAejxWGPCm1Ts6ZMKS5KyCiLE171Mk7Hx48fTr18/IiIiOHv2LBMmTECv1/PQQw+VWQxCCCGqnoMbz7J1sW36pM731adBu6pzFwBJykSBTp8+zUMPPcTFixcJCAigc+fO/PnnnwQEBDg7NCGEEJXUsd3nWf/95emTekXQskeYkyMqW5KUiQLNnz/f2SEIIYSoQs4eTuTXy9MnNb41hA4D6jg7pDInt8QQQgghhFNdOJ3G8k/3YTFbiWxZg+4PV67pkwpLkjIhhBBCOE3KBdv0SaZMMyH1fLnzico3fVJhVc13LYQQQginy0gxsWTabjJSTFSv6UnfES1wqYTTJxWWJGVCCCGEKHO50ycln8/Eu7pt+iSjR+WcPqmwJCkTQgghRJmy5FhZMXsf50/apk/q/0wrPP0q7/RJhSVJmRBCCCHKjLIq1nxzkNOHEjEY9fQb3RK/IA9nh1UuSFImhBBCiDKhlOKP/x3myPYEdHqN3v9qTmBE5Z8+qbAkKRNCCCFEmdix4jj71p8GDXo+3oSwxtWcHVK5IkmZEEIIIUrdgT/OsHVJLABd7q9P/bZBTo6o/JGkTAghhBCl6uiuBDbMiwGgTe8IWtxWtaZPKixJykSBfv/9d/r160doaCiaprFo0SKH5Uop3njjDUJCQnB3d6dnz54cPnzYOcEKIYQot878k8jqrw6iFDTpHEr7/lVv+qTCkqSsokg+A7G/236WgfT0dFq2bMnMmTMLXP7hhx8yffp0Zs+ezdatW/H09CQ6OpqsrKwyiU8IIUT5d/5UKr98utc+fVK3hxpUyemTCksmJC9rSkFORtHW2T0PVrwAygqaDnp/CK0eLto2DB5QhH8IvXv3pnfv3gUuU0oxdepUXnvtNQYMGADAt99+S1BQEIsWLeLBBx8sWmxCCCEqneTzmSydsQdTloXQ+n7cOazqTp9UWJKUlbWcDHgvtPjrKyv8Mt72KIpXzoKrZ/H3m0dsbCxxcXH07NnT3ubr60v79u3ZsmWLJGVCCFHFZaSYWDJ9N5kpJqrX9KLP081xMVTd6ZMKS1JWUWRxcXEABAU5XjkTFBRkXyaEEKJqMmWaWTpjNynnM/Gp4Ua/Z1pW+emTCktGysqawcM2alVYKWdhZjvbCFkuTQ8jt4JPEUbcDHK3ZCGEEKXLkmPll9n7uHAqDXdvA/1Gt8LTV6ZPKixJysqaphXtNGKN+tBvGiwdA8piS8j6TbW1O0lwcDAA8fHxhISE2Nvj4+Np1aqVk6ISQgjhTFarYvWcA5yJsU2fdNcomT6pqCQpqwhaD4G6PeDSMahWB3xrOjWcyMhIgoODWbt2rT0JS0lJYevWrTz99NNOjU0IIUTZU0rxxw//cHTneXQuGr2flumTikOSsorCt2aZJmNpaWkcOXLE/jo2Npbdu3dTrVo1wsPDGTNmDO+88w7169cnMjKS119/ndDQUAYOHFhmMQohhCgftv9ynP0bzoAGdzzelLBGMn1ScUhSJgq0fft2brvtNvvrcePGATB06FDmzp3LCy+8QHp6Ok8++SRJSUl07tyZlStX4ubm5qyQhRBCOMH+38/w11Lb9EldH2hAvTaBTo6o4tKUUsrZQZSllJQUfH19SU5OxsfHcWg1KyuL2NhYIiMjJbmoBOR4CiFE6Tq6M4GVX+wHBW371qZ9P7lbf0Gul3vkJbfEEEIIIUSRnY5J5NevD4CCpl1CaXdXpLNDqvAkKRNCCCFEkZw/mcovs/ZiNSvqRAXQ9aGGMn1SCZCkTAghhBCFlnw+g6Wf7CEny0LNBn7c8X9N0OkkISsJkpQJIYQQolDSk7NZMu3y9Em1vOj9dAuZPqkESVImhBBCiBvKzjSz7JM9pFzIsk2fNLolRne5iUNJkqRMCCGEENdlzrGwYtZe+/RJ/Z+V6ZNKgyRlQgghhLgmq1Wx+uuDnPknCYObnn6jW+EbINMnlQZJyoQQQghRIKUUv/83hmO7bNMn9Xm6BQHh3s4Oq9KSpEwIIYQQBfprWSwH/jhrnz6pVkN/Z4dUqUlSJoQQQoh89q0/zfblxwHo9lBDmT6pDEhSJgo0ceJENE1zeDRq1Mi+PCsri5EjR1K9enW8vLwYNGgQ8fHxToxYCCFESTmyI4Hff/gHgFvuiqRZ15pOjqhqkKSsgohLj+Ovc38Rlx5XZvts2rQp586dsz82btxoXzZ27FiWLl3KggUL2LBhA2fPnuWee+4ps9iEEEKUjtOHLrF6jm36pGbdanJL39rODqnKkBuMlDGlFJnmzCKts+ToEiZtnYQVKzp0vNz+ZfrX7V+kbbi7uBd5CgwXFxeCg4PztScnJ/PVV18xb948br/9dgDmzJlD48aN+fPPP+nQoUOR9iOEEKJ8sE2ftA+rWVG3dQBdHmgg0yeVIUnKylimOZP289oXe30rVt7d+i7vbn23SOttfXgrHoaiXcJ8+PBhQkNDcXNzo2PHjkyaNInw8HB27NhBTk4OPXv2tPdt1KgR4eHhbNmyRZIyIYSogJISMlg6Yzc52RZqNvTnjsebyvRJZUxOX4oCtW/fnrlz57Jy5UpmzZpFbGwsXbp0ITU1lbi4OFxdXfHz83NYJygoiLi4sju9KoQQomSkJ2ezdPpuMlNzqBHmRZ9/NUdvkBShrMlIWRlzd3Fn68NbC90/PiOegYsGYsVqb9NpOhYNWESQR1CR9lsUvXv3tj9v0aIF7du3JyIigv/973+4uxdtW0IIIcqv7EwzS2dcnj4pwJ1+o1vhKtMnOYX81suYpmlFOo0Y6RvJhE4TeHPLm1iVFZ2mY0LHCUT6RpZilPn5+fnRoEEDjhw5wh133IHJZCIpKclhtCw+Pr7AGjQhhBDlkznHwi+f7uXi6TTcfVzp/0wrPHxcnR1WlSVJWQVwT/176BTaiVOppwjzDiPYs+wTn7S0NI4ePcqjjz5KmzZtMBgMrF27lkGDBgEQExPDyZMn6dixY5nHJoQQouisVsXqrw5y9nASrm56+o1uiW+AnAlxJknKKohgz+AyTcbGjx9Pv379iIiI4OzZs0yYMAG9Xs9DDz2Er68vTzzxBOPGjaNatWr4+PgwevRoOnbsKEX+QghRASil2DAvhmO7z6N30dmmTwqT6ZOcTZIyUaDTp0/z0EMPcfHiRQICAujcuTN//vknAQEBAHz88cfodDoGDRpEdnY20dHRfPrpp06OWgghRGH8tTSWgxvPomlwxxNNqCnTJ5ULmlJKOTuIspSSkoKvry/Jycn4+Pg4LMvKyiI2NpbIyEjc3NycFKEoKXI8hRAiv73rTvHHD4cB6D64IU27yN36S9v1co+8nHq96++//06/fv0IDQ1F0zQWLVp03f7r16/PN/WPpmlyGwYhhBCiEA5vj+eP/9kSsnb9IiUhK2ecmpSlp6fTsmVLZs6cWaT1YmJiHKb/CQyUSVKFEEKI6zl18BJr5hwEBc271aRtn9rODklcxak1Zb1793a4H1ZhBQYG5rtxqRBCCCEKlnAihRWf7cNqUdRrE0hnmT6pXKqQt+tt1aoVISEh3HHHHWzatOm6fbOzs0lJSXF4CCGEEFVFUnwGyz7ZQ062hVqN/On5WBOZPqmcqlBJWUhICLNnz+ann37ip59+IiwsjO7du7Nz585rrjNp0iR8fX3tj7CwsDKMWAghhHCe9KRsllyePikg3JveMn1SuVahbonRsGFDGjZsaH/dqVMnjh49yscff8x3331X4Dovv/wy48aNs79OSUmRxEwIIUSll52Rw9IZu0m9mIVvoDt3jWqJq1uF+tivcir80WnXrh0bN2685nKj0YjRaCzDiIQQQgjnMpssLP90LxfPpOMh0ydVGBV+DHP37t2EhIQ4OwwhhBCiXLBarPz61QHOHUm2TZ/0TCt8asj0SRWBU0fK0tLSOHLkiP11bGwsu3fvplq1aoSHh/Pyyy9z5swZvv32WwCmTp1KZGQkTZs2JSsriy+//JLffvuNX3/91VlvQQghhCg3lFKsnxdD7J4L6F109B3Zghq1vJwdligkpyZl27dv57bbbrO/zq39Gjp0KHPnzuXcuXOcPHnSvtxkMvHcc89x5swZPDw8aNGiBWvWrHHYhhBCCFFVbV18jL83nUPT4M5hTQmtL9MnVSROPX3ZvXt3lFL5HnPnzgVg7ty5rF+/3t7/hRde4MiRI2RmZnLx4kXWrVsnCVkpudFsC0op3njjDUJCQnB3d6dnz54cPnzYoc+lS5cYPHgwPj4++Pn58cQTT5CWllaG70IIIaqOPb+dYsfKEwB0H9yIOq0CnByRKKoKX1NWVeTExZH+51ZyymhKqRvNtvDhhx8yffp0Zs+ezdatW/H09CQ6OpqsrCx7n8GDB3PgwAFWr17NsmXL+P3333nyySfLJH4hhKhK/tkWx8bL0ye171+HJp1DnRyRKI4Kf/VlRaOUQmVmFmmdpEWLiH/nXbBaQacj6LVX8Rs4sEjb0Nzdi3T35uvNtqCUYurUqbz22msMGDAAgG+//ZagoCAWLVrEgw8+yN9//83KlSvZtm0bbdu2BWDGjBn06dOHyZMnExoq/2EIIURJOHnwImvn/g1A89tq0aZ3hJMjEsUlSVkZU5mZxLRuU/wNWK3Ev/U28W+9XaTVGu7cgebhUfz95hEbG0tcXBw9e/a0t/n6+tK+fXu2bNnCgw8+yJYtW/Dz87MnZAA9e/ZEp9OxdetW7r777hKJRQghqrL42BRWfLYfq0VRv20gXe6rL9MnVWBy+lIUWdzlU6hBQUEO7UFBQfZlcXFx+SaKd3FxoVq1avY+Qgghii8xLp1ln+zBnG0hrLE/PR5rgibTJ1VoMlJWxjR3dxru3FHo/jnx8Rzre5ft1GUunY46y5dhuCoputF+hRBCVA5pibbpk7LScwiM8KbXU83Ru8g4S0UnSVkZ0zStSKcRjZGRhLz1JufemGCvKQt5602MkZGlGOX1BQcHAxAfH+9w4974+HhatWpl75OQkOCwntls5tKlS/b1hRBCFF1Wum36pLRL2fgFecj0SZVIkY/i33//zfz58/njjz84ceIEGRkZBAQEEBUVRXR0NIMGDZJpjUqY37334tm5M6YTJ3GNCMfg5KQmMjKS4OBg1q5da0/CUlJS2Lp1K08//TQAHTt2JCkpiR07dtCmja2G7rfffsNqtdK+fXtnhS6EEBWa2WThl1l7uXQ2HU9fV/qNbom7t0yfVFkUOinbuXMnL7zwAhs3buTWW2+lffv23H333bi7u3Pp0iX279/Pq6++yujRo3nhhRcYM2aMJGclyBAcXKbJ2I1mWxgzZgzvvPMO9evXJzIyktdff53Q0FAGXr4qtHHjxvTq1Yvhw4cze/ZscnJyGDVqFA8++KBceSmEEMVgtVhZ9aVt+iSjh4tMn1QJFTopGzRoEM8//zw//vgjfn5+1+y3ZcsWpk2bxpQpU3jllVdKIkbhBDeabeGFF14gPT2dJ598kqSkJDp37szKlStxc3Ozr/P9998zatQoevTogU6nY9CgQUyfPr3M34sQQlR0SinWfx/D8b0X0Bt09BnRguo1ZfqkykZTSqnCdMzJycFgMBR6w0XtX1ZSUlLw9fUlOTkZHx8fh2VZWVnExsYSGRnpkFyIikmOpxCistiy8Cg7V51A02n0fqoZkS3lbv0VyfVyj7wKfanGjRKspKSkIvUXQgghxI3tWXuKnatyp09qKAlZJVas62c/+OADfvjhB/vr+++/n+rVq1OzZk327NlTYsEJIYQQVVnM1jg2LrBNn9RhYB2a3Co1uZVZsZKy2bNnExYWBsDq1atZvXo1K1asoHfv3jz//PMlGqAQQghRFZ04cJHfvrFNn9Ti9lq0jpbpkyq7Yt3YJC4uzp6ULVu2jPvvv58777yT2rVry+0OhBBCiJsUF5vMys/2YbUq6t8SROd7ZfqkqqBYI2X+/v6cOnUKgJUrV9rnQFRKYbFYSi46IYQQoopJjEtn+Sd7MZushDepRo+hjWX6pCqiWCNl99xzDw8//DD169fn4sWL9O7dG4Bdu3ZRr169Eg1QCCGEqCrSErNYMu3y9Em1fYh+splMn1SFFCsp+/jjj6lduzanTp3iww8/xMvLdq+Uc+fOMWLEiBINUAghhKgKstJzWDJ9D2mJudMntZDpk6qYYh1tg8HA+PHj87WPHTv2pgMSQgghqpock4XlM/eSeC4dTz8j/Z5pibuXTJ9U1RQ6KVuyZEmhN9q/f/9iBSOEEEJUNRaLlVVf7Cfu2OXpk0a3xKe6TJ9UFRU6Kcud0zCXpmnknQwg71UhUuwvhBBC3JhSivX/OcSJfRfRG3T0lemTqrRCVw9arVb749dff6VVq1asWLGCpKQkkpKS+OWXX2jdujUrV64szXhFGZg0aRK33HIL3t7eBAYGMnDgQGJiYhz6ZGVlMXLkSKpXr46XlxeDBg0iPj7eoc/Jkyfp27cvHh4eBAYG8vzzz2M2m8vyrQghRLm2ZeFRDm2JQ9Np9BrejJB6fs4OSThRsS7pGDNmDNOmTSM6OhofHx98fHyIjo7mo48+4plnninpGAW2K3JOxySSlphV6vvasGEDI0eO5M8//2T16tXk5ORw5513kp6ebu8zduxYli5dyoIFC9iwYQNnz57lnnvusS+3WCz07dsXk8nE5s2b+eabb5g7dy5vvPFGqccvhBAVwe41J9n160kAbnukEbVb1HByRMLZCj0heV7u7u5s27aNZs2aObTv3buX9u3bk5mZWWIBljRnT0iulMJsshZpnUNbzvHHD/+gFGgadHmgAY06hhRpGy6uumLfePD8+fMEBgayYcMGunbtSnJyMgEBAcybN497773XFuOhQzRu3JgtW7bQoUMHVqxYwV133cXZs2cJCgoCbDNBvPjii5w/fx5X19IvYJUJyYUQ5VXMn+dYM9d2t/6Od9eVu/VXcoWdkLxYV1/ecsstjBs3ju+++87+gRsfH8/zzz9Pu3btihdxFWE2Wfn82Q3FXl8p+H3+P/w+/58irffktG4YjPpi7TM5ORmAatWqAbBjxw5ycnLsNw0GaNSoEeHh4fakbMuWLTRv3tz+9wEQHR3N008/zYEDB4iKiipWLEIIUdGd2H+R3749BEDLnmFE3Rnu5IhEeVGs05dff/01586dIzw8nHr16lGvXj3Cw8M5c+YMX331VUnHKJzIarUyZswYbr31VvvIaFxcHK6urvj5+Tn0DQoKIi4uzt4nb0KWuzx3mRBCVEVxx5JZ+blt+qQG7YO49Z56Mn2SsCvWSFm9evXYu3cvq1ev5tAhW7bfuHFjevbsKX9cN+DiquPJad0K3T8tKZv/TvyTvCeZNQ0emtgBLz9jkfZbHCNHjmT//v1s3LixWOsLIYSwuXQunWUz99imT2pajduHyPRJwlGxbxWsaRp33nknd955Z0nGU+lpmlak04j+QR50f6QR678/hLKCpoPugxvhH+RRilHajBo1imXLlvH7779Tq1Yte3twcDAmk4mkpCSH0bL4+HiCg4Ptff766y+H7eVenZnbRwghqorUS1ksnb6b7HQzQZE+9HqyOXq9TJ8kHBU7KVu7di1r164lISEBq9WxcP3rr7++6cDEFU1uDSW8STWSEzLxDXTHy790i9aVUowePZqFCxeyfv16IiMjHZa3adMGg8HA2rVrGTRoEAAxMTGcPHmSjh07AtCxY0feffddEhISCAwMBGD16tX4+PjQpEmTUo1fCCHKk6y0HJZO301aYjb+wR7cNbJlsWt8ReVWrKTszTff5K233qJt27aEhITIKcsy4OXvVurJWK6RI0cyb948Fi9ejLe3t70GzNfXF3d3d3x9fXniiScYN24c1apVw8fHh9GjR9OxY0c6dOgAwJ133kmTJk149NFH+fDDD4mLi+O1115j5MiRGI2FP+0qhBAVWU62hWUz95AYl4GXv5F+z7TCzcvg7LBEOVWspGz27NnMnTuXRx99tKTjEeXArFmzAOjevbtD+5w5c3jssccA26T0Op2OQYMGkZ2dTXR0NJ9++qm9r16vZ9myZTz99NN07NgRT09Phg4dyltvvVVWb0MIIZzKYrGy8vP9xMemXJ4+qRXe1eT2POLaipWUmUwmOnXqVNKxiHKiMLeuc3NzY+bMmcycOfOafSIiIvjll19KMjQhhKgQlFWx7ttDnDxwEReDjrtGtaRaqKezwxLlXLGqDIcNG8a8efNKOhYhhBCiUti88CgxW23TJ0U/2YzgOr7ODklUAMUaKcvKyuLzzz9nzZo1tGjRAoPB8fz4Rx99VCLBCSGEEBXNrl9Psnu1bfqk24c0onZzmT5JFE6xkrK9e/fSqlUrAPbv3++wTIr+hRBCVFWH/jzH5p+PANDpnno06lC0KfFE1VaspGzdunUlHYcQQghRoR3fd8E+fVKrO8Jl+iRRZDd957rTp09z+vTpkoil3CjGHO2iHJLjKIQoK+eOJrPq8/0oq6Jhh2A63V3X2SGJCqhYSZnVauWtt97C19eXiIgIIiIi8PPz4+233853I9mKJLc2LiMjw8mRiJKQexyvrnkUQoiSdPFsGstn7sGcYyWieXVue7SRTJ8kiqVYpy9fffVVvvrqK95//31uvfVWADZu3MjEiRPJysri3XffLdEgy4per8fPz4+EhAQAPDw8pEauAlJKkZGRQUJCAn5+fuj1cudsIUTpsE2ftIfsDNv0SdHDm8n0SaLYNFWMczyhoaHMnj2b/v37O7QvXryYESNGcObMmRILsKSlpKTg6+tLcnIyPj4++ZYrpYiLiyMpKansgxMlys/Pj+DgYEmshRClIjPNxMLJO0mMy8A/2IN7xreRu/WLAt0o98hVrJGyS5cu0ahRo3ztjRo14tKlS8XZZLmhaRohISEEBgaSk5Pj7HBEMRkMBhkhE0KUmpxsC8tn7pXpk0SJKlZS1rJlSz755BOmT5/u0P7JJ5/QsmXLEgnM2fR6vXyoCyGEyMc2fdI+2/RJni70e0amTxIlo1hJ2Ycffkjfvn1Zs2YNHTt2BGDLli2cOnVKptURQghRaSmr4rdv/ubkgUu4uOq4a2RLqoXI9EmiZBSrGrFbt27ExMRw9913k5SURFJSEvfccw8xMTF06dKlpGMUQgghnE4pxaafjvDPX/HodBq9nmwu0yeJElWsQv+KrLDFdkIIIUReO1edYMvCowD0fLwJDdsHOzkiUVEUNvco1kjZnDlzWLBgQb72BQsW8M033xRnk0IIIUS59ffms/aE7NZ760lCJkpFsZKySZMmUaNG/glWAwMDee+99246KCGEEKK8iN17gXX/iQEg6s5wWvWU6ZNE6ShWUnby5EkiIyPztUdERHDy5MmbDkoIIYQoD84dSWLVF7bpkxp1DKajTJ8kSlGxkrLAwED27t2br33Pnj1Ur179poMSQgghnCktMYuDm86y7JM9WHKs1G5endseaSQ3oxalqli3xHjooYd45pln8Pb2pmvXrgBs2LCBZ599lgcffLBEAxRCCCHK0sFNZ1n3n0Nw+TI43wB37hzeDJ1MnyRKWbGSsrfffpvjx4/To0cPXFxsm7BarQwZMkRqyoQQQlRYaYlZrM+TkAGkXMgkOz0Hg6vcUFyUrmIlZa6urvzwww+8/fbb7NmzB3d3d5o3b05ERERJxyeEEEKUmXNHk7n6RlFKQXJCJl7+ctd+UbqKlZTlql27Nkop6tatax8xE0IIISqilAuZbP7pSL52TQe+ge5OiEhUNcU6QZ6RkcETTzyBh4cHTZs2tV9xOXr0aN5///0SDVAIIYQobUnxGSycspO0xGzcvAzk1vNrOug+uJGMkokyUazhrZdffpk9e/awfv16evXqZW/v2bMnEydO5KWXXiqxAIUQQojSdOlsOoun7SIj2YR/sAcDxkShlCI5IRPfQHdJyESZKVZStmjRIn744Qc6dOjgcHlw06ZNOXr0aIkFJ4QQQpSmC6dTWTJtN5mpOVSv6Un/Z6Pw8HEFkGRMlLliJWXnz58nMDAwX3t6errcw0UIIUSFkHAihSXTdpOdYSYg3Jv+z7TCzcvg7LBEFVasmrK2bduyfPly++vcROzLL7+kY8eOhd7O77//Tr9+/QgNDUXTNBYtWnTDddavX0/r1q0xGo3Uq1ePuXPnFjV8IYQQVVzcsWQWf7yL7AwzwXV8GDBGEjLhfMUaKXvvvffo3bs3Bw8exGw2M23aNA4ePMjmzZvZsGFDobeTnp5Oy5Yt+b//+z/uueeeG/aPjY2lb9++/Otf/+L7779n7dq1DBs2jJCQEKKjo4vzVoQQQlQxZ/5JZNnMvZizLYTW96PvyBa4uskdBITzaUpdfUeWwjl69Cjvv/8+e/bsIS0tjdatW/Piiy/SvHnz4gWiaSxcuJCBAwdes8+LL77I8uXL2b9/v73twQcfJCkpiZUrVxZqPykpKfj6+pKcnIyPj0+xYhVCCFExnTx4kRWz9mHOsVKrkT99RrSQm8KKUlfY3KPYXw3q1q3LF198UdzVi2XLli307NnToS06OpoxY8Zcc53s7Gyys7Ptr1NSUkorPCGEEOXY8b0XWPH5PqxmRUTz6vR6shkuBknIRPlRrJqynTt3sm/fPvvrxYsXM3DgQF555RVMJlOJBXe1uLg4goKCHNqCgoJISUkhMzOzwHUmTZqEr6+v/REWFlZq8QkhhCifju5MYMVsW0JWJyqA3k81l4RMlDvFSsqeeuop/vnnHwCOHTvGAw88gIeHBwsWLOCFF14o0QBv1ssvv0xycrL9cerUKWeHJIQQogz981ccq748gNWqqH9LENHDmqJ3kcnFRflTrL/Kf/75h1atWgGwYMECunXrxrx585g7dy4//fRTScbnIDg4mPj4eIe2+Ph4fHx8cHcveAoMo9GIj4+Pw0MIIUTV8Pfms6yecxBlVTTqGEzPx5ug00tCJsqnYv1lKqWwWq0ArFmzhj59+gAQFhbGhQsXSi66q3Ts2JG1a9c6tK1evbpIt+EQQghRNezfcJrfvj0ECpp2rcntjzZGp5N7aYryq9j3KXvnnXf47rvv2LBhA3379gVst6y4uubretLS0ti9eze7d++2r7979277XJovv/wyQ4YMsff/17/+xbFjx3jhhRc4dOgQn376Kf/73/8YO3Zscd6GEEKISmr3mpNs+K+tzKbl7WF0e6gBmiRkopwrVlI2depUdu7cyahRo3j11VepV68eAD/++COdOnUq9Ha2b99OVFQUUVFRAIwbN46oqCjeeOMNAM6dO2dP0AAiIyNZvnw5q1evpmXLlkyZMoUvv/xS7lEmhBDCbvuK42z68QgArXtFcOt99WS2GVEhFPs+ZQXJyspCr9djMJTfuyLLfcqEEKJyUkrx19JYtv9yHIB2/SJp26e2JGTC6Ur8PmVKqRv+Ybu5yeStQgghyp5Sii0/H2XXatvZlY5316V1dISToxKiaAp9+rJp06bMnz//hvchO3z4ME8//TTvv//+TQcnhBBC3IiyKv7432F7Qtb5/vqSkIkKqdAjZTNmzODFF19kxIgR3HHHHbRt25bQ0FDc3NxITEzk4MGDbNy4kQMHDjBq1Ciefvrp0oxbCCGEQFkV6+fFcHDjWdCg+8MNadqlprPDEqJYilxTtnHjRn744Qf++OMPTpw4QWZmJjVq1CAqKoro6GgGDx6Mv79/acV706SmTAghKgerxcpv3x4iZmscmga3D21Mow4hzg5LiHwKm3uUaKF/RSBJmRBCVHwWi5U1Xx/kyI4ENJ3GHf/XhPptC39LJiHKUqlPSC6EEEI4gyXHyqov9xO75wI6vUb0sGbUiQpwdlhC3DRJyoQQQlQYZpOFFZ/t4+SBS+hddPR6qhm1m9dwdlhClAhJyoQQQlQIOdkWln+6lzMxibi46ugzogVhjao5OywhSowkZUIIIco9U6aZZTP3cO5IMgY3PXeNbElofT9nhyVEiZKkTAghRLmWlZ7D0hl7SDiegqu7C/2eaUlwpK+zwxKixBVr7kuAo0eP8tprr/HQQw+RkJAAwIoVKzhw4ECJBSeEEKJqy0wzsXjqLhKOp+DmaWDg2ChJyESlVaykbMOGDTRv3pytW7fy888/k5aWBsCePXuYMGFCiQYohBCiakpPzmbRR7u4cCoNd28DA8dFERDu7eywhCg1xUrKXnrpJd555x1Wr16Nq6urvf3222/nzz//LLHghBBCVE1piVks+mgXl86m4+nryt3PtaZ6TS9nhyVEqSpWUrZv3z7uvvvufO2BgYFcuHDhpoMSQghRdaVcyGThlJ0kxWfgVc3I3eNb4x/s6eywhCh1xUrK/Pz8OHfuXL72Xbt2UbOmzDkmhBCieJISMlg4ZScpF7LwCXDn7uda4xvg4eywhCgTxUrKHnzwQV588UXi4uLQNA2r1cqmTZsYP348Q4YMKekYhRBCVAGXzqWzcMpO0hKz8Q/24O5xrfGp7u7ssIQoM8VKyt577z0aNWpEWFgYaWlpNGnShK5du9KpUydee+21ko5RCCFEJXfhdBqLPtpJRrKJ6jU9GTiuNV7+RmeHJUSZuqkJyU+ePMn+/ftJS0sjKiqK+vXrl2RspUImJBdCiPIl4UQKS6bvJjvdTEC4N/2faYWbl8HZYQlRYspkQvLw8HDCw8NvZhNCCCGqsLhjySydvhtTloWgSB/6jW6J0UMSMlE1FSspU0rx448/sm7dOhISErBarQ7Lf/755xIJTgghROV19nAiyz7ZS062hZB6vtw1qiWubjLRjKi6ivXXP2bMGD777DNuu+02goKC0DStpOMSQghRiZ06eIlfZu3FnGOlViN/+jzdAoNR7+ywhHCqYiVl3333HT///DN9+vQp6XiEEEJUcsf3XmDl5/uxmK1ENKtOr6ea4WKQhEyIYiVlvr6+1KlTp6RjEUIIUckd3ZXAr18ewGpR1GkVwJ3DmqJ3KfY0zEJUKsX6lzBx4kTefPNNMjMzSzoeIYQQldQ/2+JY9YUtIavfNpA7h0tCJkRexRopu//++/nvf/9LYGAgtWvXxmBwvFJm586dJRKcEEKIyuHvzef47bu/QUGjDsHcNqQxOp3UIwuRV7GSsqFDh7Jjxw4eeeQRKfQXQghxXft/P8OGeTEANO0SSreHGqJJQiZEPsVKypYvX86qVavo3LlzSccjhBCiEtmz9hQbFxwGoMXtteh8X335Ii/ENRQrKQsLC5O74QshhLiuHSuP8+eiYwC0jo6gw8A6kpAJcR3FqrCcMmUKL7zwAsePHy/hcIQQQlR0Sim2Lj1mT8huuStSEjIhCqFYI2WPPPIIGRkZ1K1bFw8Pj3yF/pcuXSqR4IQQQlQsSim2LDzKrl9PAtDx7rq0jo5wclRCVAzFSsqmTp1awmEIIYSo6JRSbPzfYfauOw1A5/vq07JHmJOjEqLiKPbVl0IIIUQuZVWs/28MB/84C0C3hxvSrGtNJ0clRMVS6KQsJSXFXtyfkpJy3b5yEYAQQlQdVouV3747RMyfcWga3D6kMY06hjg7LCEqnEInZf7+/pw7d47AwED8/PwKLNhUSqFpGhaLpUSDFEIIUT5ZLFbWzDnIke0JaDqNOx5vQv1bgpwdlhAVUqGTst9++41q1aoBsG7dulILSAghRMVgybGy6sv9xO65gE6vET2sGXWiApwdlhAVVqGTsm7dutmfR0ZGEhYWlm+0TCnFqVOnSi46IYQQ5ZLZZGHFZ/s5eeAiehcdvZ5qRu3mNZwdlhAVWrHuUxYZGcn58+fztV+6dInIyMibDkoIIUT5lZNtYdnMvZw8cBEXg46+I1tIQiZECSjW1Ze5tWNXS0tLw83N7aaDEkIIUT6ZMs0sm7mHc0eSMRj13DWqJaH1/ZwdlhCVQpGSsnHjxgGgaRqvv/46Hh4e9mUWi4WtW7fSqlWrEg1QCCFE+ZCVnsPSGXtIOJ6Cq7sL/Ua3JLiOr7PDEqLSKFJStmvXLsA2UrZv3z5cXV3ty1xdXWnZsiXjx48v2QiFEEI4XWaaiSXTdnPhVBpGTxcGPBtFQLi3s8MSolIpUlKWe9Xl448/zrRp0+R+ZEIIUQVkpJhYPHUXl86m4+5tYMCYKKrX9HJ2WEJUOsWqKZszZ05JxyGEEKIcSkvMZvHUXSTFZ+Dp68qAsVH4B3s6OywhKqViJWVCCCEqv5SLmSz+eBcpF7LwqmZk4NgofAM8bryiEKJYJCkTQgiRT1JCBoun7iLtUjY+NdwYMDYKn+ruzg5LiBIXlx7HyZSThPuEE+wZ7NRYJCkTQgjhIDEunUUf7yIj2YRfkAcDxkTh5W90dlhClLifD//Mm5vfxIoVnaZjQscJ3FP/HqfFU6ybxwohhKicLp5JY+GUnWQkm6gW6sndz7WWhExUSrHJsUzcPBErVgCsysqbW94kLj3OaTFJUiaEEAKAhBMpLPxoJ5mpOdQI82LguCg8fFxvvKIQFYjFauHnwz/zyC+PoFBUS1E0PWGlWorCqqycSnXedJFy+lIIIQRxx5JZOmMPpkwzQZE+9BvdEqOHwdlhCVGiNp/dzJTtU/gn8R8Abttj5akVVnQKrBp80VtP2L1hTotPkjIhhKjizh5OZNkne8nJthBSz5e7RrbE1V0+HkTlcSTxCFO2T2b/PxsJO68YdMlIz7QIqm0+RO6kkToFT660Uv0FwEl3fZF/dUIIUYWd+vsSv3y6F3OOlVqN/OnzdAsMRr2zwxLipliSk8k+fJiLB3axe+tSTIeP8Ph5hXdWbo9M4FC+9TSrwnTiJIZg51yFKUmZEEJUUcf3XWDlZ/uxmK1ENKtOryeb4eIqCZmoOKwZGWQfPUb24cO2xz//kH34MOaEBHufenlX0OlwrV0bY/36uISEkPjNN6CU4/KI8DKL/2qSlAkhRBV0bNd5Vn25H6tFEdmyBtHDmqE3yLVfonxSJhPZx49fSb4OHyH78GFyTp1yTKrySPCFUzU0ssIDaNvpHhrdEo1rZCQ645Wrid3q1eXcGxPAagWdjpC33nTaKBlIUiaEEFXO4W3xrJ5zEGVV1GsbSM/Hm6DXS0ImnE9ZreScPp1n5Ovyz+PHISenwHX0NWpgrF+P5Jq+rNAOsNX9HKdrgG+1EJ5t/Sx9Ivug0wr++/a79148O3fGdOIkrhHhTk3IQJIyIYSoUg5tOcdv3/6NUtCwQzC3D2mMTqfdeEUhSpBSCnPCeYdTjtmHD5N99CgqM7PAdXReXhjr13d8NKjPGZdUPt7xMWtPrgXA0+DFk82H8UjjR3BzcbthLIbgYKcnY7kkKRNCiCpi/+9n2DAvBoAmXULp/lBDNEnIRCmzJCWRffgwWfZTj7bTj9bk5AL7a66uuNari1v9BhgbXEnAXIKD0bQrf69JWUl8svcz5h+aj1mZ0Wk67q1/LyNajaC6e/WyenslSpIyIYSoAvasPcXGBYcBaHFbLTrfX9/hA06Im2Uruj965ZRjbtH9+fMFr6DX24vujfXr2ZMv1/BwNP21LzgxWUz899B/+WzvZ6SaUgHoUrMLz7V9jrp+dUvjrZUZScqEEKKS27HyOH8uOgZA6+hwOgysKwmZKDZlMpEdm7fo/vCVovtrMNSs6XDK0digga3o3rXwM0YopVh9YjUf7/iY02mnAWjg34DxbcfTMbTjTb+v8qBcJGUzZ87k3//+N3FxcbRs2ZIZM2bQrl27AvvOnTuXxx9/3KHNaDSSlZVVYH8hhKiqlFJsWxbLtuXHAbilb21uuStSEjJRKMpicSy6z33EHgezucB19AE1cLuq7su1bj30Xjd3N9a95/cyeftkdiXsAiDAPYDRUaPpX7c/el3luY2L05OyH374gXHjxjF79mzat2/P1KlTiY6OJiYmhsDAwALX8fHxISYmxv5a/oMRQghHSin+XHSUnatOAtBhYB3a9Krt3KBEuWQruk+wnW7857Bj0f01Bjx03t75i+7r18OlWrUSje1M2hmm7ZjGiuMrAHDTu/FYs8d4vOnjeBg8SnRf5YHTk7KPPvqI4cOH20e/Zs+ezfLly/n666956aWXClxH0zSCC3mlRHZ2NtnZ2fbXKSkpNx+0EEKUY0opNv7vMHvX2U7xdL6vPi17OG8+P1F+mBMTrxr5st3vy3qNz0bNaMRYty7GBg2unHqsXx+XoKBSHRBJNaXyxb4v+P7g95isJjQ0+tftz+io0QR5BpXafp3NqUmZyWRix44dvPzyy/Y2nU5Hz5492bJlyzXXS0tLIyIiAqvVSuvWrXnvvfdo2rRpgX0nTZrEm2++WeKxCyFEeaSsig3/jeHAH2cB6PZwQ5p1renkqERZs6an24ru8xTcZx0+jOX8hYJX0OtxjaztMPLlVr8+hrCw6xbdl7Qcaw4//vMjs3bPIjE7EYD2we0Zf8t4GlVrVGZxOItTk7ILFy5gsVgICnLMeoOCgjh0KP+cVAANGzbk66+/pkWLFiQnJzN58mQ6derEgQMHqFWrVr7+L7/8MuPGjbO/TklJISxMvjEKISofq1Wx7tu/OfRnHGhw+6ONadwpxNlhiVJkNZkwxcY6nnY8fJic06evuY6hVq08Rfe2ETDXyNpFKrovaUopfj/9O1N2TCE2ORaASN9IxrcdT5eaXapMmZLTT18WVceOHenY8cpVFp06daJx48Z89tlnvP322/n6G41GjHmmVBBCiMrIYrGyds5BDm9PQNNp9Hy8MQ1uKR83xBQ3T1ks5Jw6ddW9vg5jOn7imkX3LgEBjlc81q+PsW5ddJ43V3Rf0g5dOsTkbZPZGrcVAH+jPyNajWBQg0EYdAYnR1e2nJqU1ahRA71eT3x8vEN7fHx8oWvGDAYDUVFRHDlypDRCFEKIcs+SY+XXrw5wbPd5dHqNO4c1pW5UwRdKifJNKYU5Pv7KXe7/yVN0n6c+Oi+dj0++e30Z69fHxd+/jKMvmvj0eGbsmsGSo0tQKFx1rjzS5BGGNR+Gt6u3s8NzCqcmZa6urrRp04a1a9cycOBAAKxWK2vXrmXUqFGF2obFYmHfvn306dOnFCMVQojyyZxjYeVn+zmx/yJ6Fx29nmpG7eY1nB2WKARzYmK+047Zhw9jTU0tsL/m5uZYdH95BMwlMLBCnd7LyMlgzoE5fHPgGzLNtimVekf25tnWz1LTq2rXPzr99OW4ceMYOnQobdu2pV27dkydOpX09HT71ZhDhgyhZs2aTJo0CYC33nqLDh06UK9ePZKSkvj3v//NiRMnGDZsmDPfhhBClLmcbAu/zNrL6UOJuBh09BnRgrDGJXtLAnHzLGnpmI7arnLMss/zeATLhWsU3bu4YLyq6N5Yvz6GWrXKtOi+pFmsFhYfXcyMXTO4kGl771GBUYxvO54WAS2cHF354PSk7IEHHuD8+fO88cYbxMXF0apVK1auXGkv/j958iQ63ZXZ3RMTExk+fDhxcXH4+/vTpk0bNm/eTJMmTZz1FoQQosyZMs0sm7mHc0eSMRj13DWqBaH1y/fpqsrOajJhOnbM8bTj4cPknDlzzXUMYWH5675q10ZzYtF9adh8djNTtk/hn8R/AAjzDmNsm7H0DO9ZoUb5SpumlFLODqIspaSk4OvrS3JyMj4+Ps4ORwghiiw7I4elM/YQH5uCq5uefs+0IriOr7PDqlRy4uIwHT+Ba+0IDFfVOCuLBdPJk473+vrnH0wnToDFUuD2XAIDHUe+GtTHWKdOuSu6L2lHEo8wZccUNp7ZCIC3qzf/avEvHmz0IK76ypV4Xk9hcw+nj5QJIYQovKy0HJZM3835k6kYPV3o/0wrAiPkC2ZJSvrxR869MQGsVtDp8B88GENQoP1eX6ajx65fdN/A8V5fxvr10fv5le2bcLILmRf4dPen/HT4J6zKiovmwoONHuSpFk/h5+bn7PDKLUnKhBCigshIMbF46i4unU3H3dtA/2ejqFHLy9lhVSpZ/xzm3OtvQO5JJKuVxO++y9dPc3fHWK9evrovl8CAKn06LsucxXcHv+Or/V+RnpMOQI/wHoxtM5YInwgnR1f+SVImhBAVQFpiNoun7iIpPgMPX1cGjImiWkjlPvVVVpTJRNoff5C8eAmpa9deScjy8GjfHs+OHRyL7vPUO1d1VmVl+bHlTN81nbj0OACaVm/K+LbjaRvc1snRVRySlAkhRDmXcjGTxR/vIuVCFl7+RgaMjcIvsPJNxlyWlFJk7dlD8pIlpPyyAktS0rU763SEfvB+vtoyYbMjfgf/3vZvDlw8AECwZzDPtn6WPpF90GmSuBaFJGVCCFGOJZ/PYNHHu0i7lI1PDTcGjInCp4a7s8OqsEynTtkSsSVLbYX5l7kEBODTrx++A/qTtW+fQ01ZyFtvSkJWgBMpJ/h4x8esPbkWAE+DJ8OaD+ORxo/g5uLm5OgqJknKhBCinEqMS2fxx7tITzbhF+TBgDFRePnLtHFFZUlOJmXFSpKXLCFz5057u+bujvcdPfEdMADPDh3s9wBza9gQz86dMZ04iWtEuCRkV0nOTmb2ntnMPzQfszKj03TcW/9enm71NDXc5cbFN0OSMiGEKIcunklj8dRdZKbmUC3Uk/7PtsLTVxKywrKaTKRt2EDKkqWkrV+PysmxLdDp8OzQAd8B/fHu2fOat6QwBAdLMnYVk8XEfw/9l8/2fkaqyTbrQOeanXmuzXPU86/n5OgqB0nKhBCinDl/MpXF03aRnW6mRpgX/Z9thbtX1bmnU3EppcjctZvkJYtJXbESS3KyfZmxUSN8+/fHp29fDEEyL2hRKKVYfWI1H+/4mNNppwGo71+f8W3H0ym0k5Ojq1wkKRNCiHIk7lgyS2fswZRpJrC2D/1Gt8TN0+DssMo108mTJC9eQvLSpeScPGlvdwkMxKffXfj2H4BbwwZOjLDi2nt+L5O3T2ZXwi4AarjXYHTUaAbUHYBeV3GnfCqvJCkTQohy4uzhJJZ9soecbAsh9Xy5a2RLXN3lv+mCWJKSSFmxguTFS8jcvdvernl44HPHHfgO6I9H+/YVeq5IZzqTdoZpO6ax4vgKANz0bjzW7DEeb/o4Hga58re0yL92IYQoB04dusQvn+7FbLJSs6E/fUe0wGCUhCIvq8lE2vr1JC9ZQtqG3yFvnVinTrY6sR490HlI0lBcqaZUvtj3Bd8f/B6T1YSGRv+6/RkdNZogzyBnh1fpSVImhBBOdnzfBVZ+th+L2Up40+r0fqoZLq6SkEFundgukhcvIWXlSqx568QaN75cJ9YHQ6DUid2MHGsOP/7zI7N2zyIxOxGA9sHtGX/LeBpVa+Tk6KoOScqEEMKJju06z6ov92O1KCJb1iB6WDP0Brnhpun4cZKXLLXViZ06ZW93CQrCt99d+PTvj1sDqRO7WUopfj/9O1N2TCE2ORaASN9InmvzHF1rda3SU0Y5gyRlQgjhJIe3xbN6zkGUVVGvTSA9/68Jen3VTcjMiYmkrFhByuIlZO7ZY2/XeXjgfeedtjqxdu2kTqyEHLp0iMnbJrM1bisA/kZ/RrQawaAGgzDo5OISZ5CkTAghnODQlnP89u3fKAUN2wdz+5BG6KpgQmY1mUhbd7lO7Per6sRuvRXf/v3x7tkDnbvMYlBS4tPjmbFrBkuOLkGhcNW58kiTRxjWfBjert7ODq9Kk6RMCCHK2IE/zrD++xgAmnQOpfvDDdF0Vec0kVKKzB07SF6y1FYnlpJiX2ZsYqsT8+3bF5eAACdGWflk5GQw58AcvjnwDZnmTAB61+7Ns22epaZXTSdHJ0CSMiGEKFN7fjvFxv8dBqD5bbXocn/9KlO3kx0ba593MufMGXu7S3Awvv3uwrd/f4z16zsxwsrJYrWw+OhiPtn1CeczzwPQKqAVz9/yPC0CWjg5OpGXJGVCCFFGdq46wZaFRwGIuiOcjvfUrfQJmTkxkZTlv5C8ZAlZe/fa23UeHnhHR1+pE9NVvVO3ZWHz2c1M2T6FfxL/AaCWVy3GthnLHRF3VPq/vYpIkjIhhChlSim2LT/OtmW2q9va9q1Nu7siK+2HojU7m7R160hevIS0P/4As9m2QK/H89ZO+PYfgHeP26VOrBQdSTzClB1T2HhmIwDert481eIpHmr0EK56mbKrvJKkTAghSpFSij8XHWPnqhMAdBhYhza9ajs3qFKgrNbLdWJLSFm5Cmtqqn2ZW9Om+A7oj0+fPrjUqOHEKCu/C5kX+HT3p/x0+CesyoqL5sKDjR7kqRZP4efm5+zwxA1IUiaEEKVEKcXGBYfZ+5ttEufO99WnZY8wJ0dVsrKPxZK8ZLGtTuzsWXu7S0gIvv364du/H8Z69ZwYYdWQZc7iu4Pf8dX+r0jPSQegR3gPxrYZS4RPhJOjE4UlSZkQQpQCZVVs+G8MB/6wJSrdHmpAs261nBxVyTBfunSlTmzfPnu7ztMT717R+PYfgMctbaVOrAxYlZXlx5Yzfdd04tLjAGhSvQnPt32etsFtnRydKCpJyoQQooRZrYp13/3NoS1xoMHtjzaicadQZ4d1U6xZWVfqxDZudKgT8+rcGd8B/fG6/XZ0bm7ODbQK2RG/g39v+zcHLh4AINgzmGeinqFvnb7oNEmIKyJJyoQQogRZLFbWzjnI4e0JaDqNno81pkG7YGeHVSzKaiVj+3aSlywhdeUqrGlp9mVuzZrZ5510qV7diVFWPSdSTvDxjo9Ze3ItAJ4GT4Y1H8YjjR/BzUWS4opMkjIhhCghFrOVX788wLHd59HpNe58oil1W1e8ibKzjx4lefESkpctxXz2nL3dJTQE3379bXVides6McKqKTk7mdl7ZjP/0HzMyoxO03Fv/Xt5utXT1HCXCygqA0nKhBCiBJhzLKz8fD8n9l1E56LR+8nm1G5RcT4ozRcvkrJ8OcmLl5B14IC9XefldblOrD8ebaVOzBlMFhP/PfRfPtv7Gakm21WtnWt25rk2z1HPXy6iqEwkKRNCiJuUk23hl1l7OX0oEReDjj5PtyCsSTVnh3VD1qwsUteuJXnJEtI3bgKLxbbAxeVKndhtt0mdmJMopVh9YjUf7/iY02m2K3jr+9dnfNvxdArt5OToRGmQpEwIIW6CKcvMsk/2cO5IMi5GPXeNbEHNBv7ODuualNVKxl/bbHViq1ZhTU+3L3Nr3vxKnVi18p9UVmZ7z+9l8vbJ7ErYBUAN9xqMjhrNgLoD0Ov0To5OlBZJyoQQopiyM3JYOmMP8bEpuLrpuWt0K0Lq+jo7rAJlHzlyuU5sGeZzV+rEDKGh+PTvZ5t3sk4dJ0YoAM6knWHajmmsOL4CADe9G481e4zHmz6Oh8HDydGJ0iZJmRBCFENWWg5Lpu/m/MlUjB4u9H+2FYERPs4Oy4H5woUrdWIHD9rbdd7e+FyuE3Nv00bqxMqBVFMqX+z7gu8Pfo/JakJDo3/d/oyOGk2QZ5CzwxNlRJIyIYQooowUE0um7eLimXTcvQ30fzaKGrW8nB0WANbMTFLX/kbyksWkb9rsWCfWpcuVOjGj0bmBCgByrDn8+M+PzNo9i8TsRADaB7fnubbP0bh6YydHJ8qaJGVCCFFIaYlZnDuazNbFR0k+n4WHrysDxkRRLcTTqXHZ6sT+InnxElJ//dWxTqxlC1udWO/eUidWjiil+P3070zZMYXYZNtE9bV9avNc2+foVqtbpZ2sXlyfJGVCCFEIBzedZd1/DoGyvTZ6uHD3c63xC3RenU/24cMkL1lC8tJlmOPi7O2GmjWv1IlFRjotPlGwQ5cOMXnbZLbGbQXA3+jP062e5t4G92LQGZwcnXAmScqEEKIAFouVtEvZpF7K4vzJVDb/dMRhuSnTjIuh7GuxzOfPk7x8OclLlpB98G97u61OrBe+A/rj3rq11ImVQ/Hp8czYNYMlR5egUBh0Bh5p8gjDmw/H29Xb2eGJckCSMiFElZSTbSH1UpbtcTHPz8vP05Oz7aNiBVEKkhMy8fIv/Xt4WTMzSV1z+X5imzaB1WpbYDDg1bUrvv3749W9m9SJlVMZORnMOTCHbw58Q6Y5E4DetXvzbJtnqelV08nRifJEkjIhRKWjlCI7w+yYbF2VgGWl5dxwO3oXHV7VjLh7uxJ3NNlhmaYD30D30noLKIvFsU4sI8O+zL1lS3wGXK4T8y+/90Sr6ixWC4uPLuaTXZ9wPvM8AK0CWvH8Lc/TIqCFk6MT5ZEkZUKICkdZFRkppvyjXHkSr5xsyw234+qmx7u6G97VbA+v3OeXf3p4u6LpbAXXBzedZf33h1BWW0LWfXCjUhkly4r5h+Qli0lZthxzfLy93VCrFr79bfNOutauXeL7FSVr89nNTNk+hX8S/wGgllctxrYZyx0Rd0gRv7gmScqEEOWOxWIlPTG7wBGulItZpCVmYTVf59ziZe7eBock6+qfRo/CF1U3uTWU8CbVSE7IxDfQvUQTspyEBFKWXa4TO3TI3q7z8cGnd29bnVhUlHyYVwBHk44yZfsU/jjzBwDert481eIpHmr0EK56VydHJ8o7ScqEEGUux2TJP8J1MYu0ywlYelI26gY5l6aBp5/RYaQrb9LlVc0Ng2vJTkfj5e9WYsmYNSOD1DVrSF68hPQtWxzrxLrl1ol1R+cqH+QVwYXMC8zaPYufDv+ERVlw0Vx4oNED/KvFv/Bz83N2eKKCkKRMCFGiSrqeq8CRrmpuePob0esr1hWGymIh/c8/SVmyhJTVa1B568RatcJ3QH+8e/WSOrEKJMucxXcHv+Or/V+RnmO7P1yP8B6MaT2G2r61nRucqHAkKRNCFImyKjJSTddNunKyblzPZXDT413NDZ/qN67nquiyYmJIXryElGXLMCck2NsN4eH49utnqxOLiHBihKKorMrK8mPLmb5rOnHptnvENanehOfbPk/b4LZOjk5UVJKUCSEcXK+eK/ViFqlFrefKm3A51HO5VOoaqZz4BFKWLbPVicXE2Nt1vr749O6Fb/8BuEe1qtS/g8pqR/wO/r3t3xy4eACAYM9gnol6hr51+qLTKtborShfJCkToorJMVlstVt5CucrQj1XRWBNT79SJ/bnnw51Yt7du+HTvz9e3bpJnVgFdSLlBB/v+Ji1J9cC4OHiwbDmw3i0yaO4uZT+/epE5SdJmRCViL2e66rTiml5nmem3rieS+ei4e1/OdGqJPVcpUVZLKRv+ZPkJYtJXb0GlZlpX+YeFYXvgP749OqF3s/PeUGKm5KcnczsPbOZf2g+ZmVGp+kYVH8QI1qNoIZ7DWeHJyoRScqEqEBKup7rWiNdlameqyTlxMVhOn4C19oRWJKSrtSJnT9v72MID79yP7HwcCdGK25GXHocR5OOsithF/MOzSPVlApA55qdea7Nc9Tzr+fkCEVlJEmZEOWIvZ7rGtP/pCVmYzFbb7idql7PVRoSF/xI3IQJV05J5qH39cW7T298+/fHvZXUiZV3SilyrDlkmjPJMmeRZckiy5xle23JYt3Jdfz30H9ReebZqu9fn/Ftx9MptJMTIxeVnSRlQtyEtMQskhIy8SvkzUSvrue6eqRL6rlKj8rJwZKWhjUlBUtqGta0VCwpKVjtz1NtP1PTsKZe7pOaiiU1FUtyMtbk5Hzb9OzaFf/778Ora1c0qRMrEVZldUiUssxZZFoy7c+vfp1pzrQnU/Y+lqwrCdfVry/3s6gbjyjn0tD45LZPCPUOLcV3LoQkZUIU28FNZ1n/n0MoZUuUug1uSN2owJKt5yrgFGNVrOdSVivW9HRbQpV2OVmyJ1Gp9uTJmpKKJS0Va2oaltTLCdflZSorq8Tjqv7EE3i2b1fi2y2vcqw510yOrk6EMs2Z111+9ehU7vNsS3aZvie9psfdxR03FzfcXdyxKitn0s449FEozqSfkaRMlDpJyoS4DovZSlZ6DllpOWSl55CdbiYrPYfkC5nsXHnC3k8pWP+fGNb/J+Y6W7OpavVcSilUZqbj6FS+xCr/6JQ19XKClZKKNT2dGw4hFpLm4YHeywudjzd6L2903t7ovXN/eqHz9kHn7WVr8/JC7+ODNSubU8OHO5661OlwjSgfNWNKKbIt2QWOChU0YnSjUaSCRqOyzFmYlblM35dRb8TNxQ03vS1hyk2e3PRutp+XEymH1/orCdb1luc+DDrHqbbi0uOI/ikaq7pyrHWajjDvsDJ976JqkqRMVAlWq8KUYUuoHB5pOWRnmO1Jl+PDjLkQk1pfzc3LkP+GqBW4nkuZTAWc9sszSnWd0SlbYpUG5pL5MNcMBnQ+PrakytsbvY83Oi/vy0lUnmTK28eWYHld7pObYHl5oRkKP99lXiFvvcm5Ny7XlOl0hLz1Jobg4BuuZ7FaCnU6zSFZKmAUqaDXebeTt/6ptOk0nT3RsSdL10mUcpOqvK89XDwckiOHZOnyus6451ewZzATOk7gzS1vYlVWdJqOCR0nEOx542MtxM3SlCqhr58VREpKCr6+viQnJ+Pj4+PscEQRKaXIybY4JFG5o1eOyZaZ7Iw8I1yZZor7maVpYPQw4OZlwM3TBaOnAb2LjmO7zufr99DEDvgHeZTAOy0ZymLBmp5uT6IckqUbjE7l/lTZJXQ6SaezJVJeXo6J1eVRqvyJ1eVleRMro7FkYsF2Ki7bnO2Q5GRbsu2n0HKTnty2HfE72LlvNUGJVuL8NcLrRRHsEXzN03i5yVKO9canrEuSQWfIl+TYR42uSnyuTpQcRpeu7pMnCTPoDBXqi0VxxKXHcSr1FGHeYZKQiZtW2NxDRsqE05hzLFcSqrQcsvIkUVnpZrKvGrWyJWA5WC3F/x7h6qbH6GnAzfNykuXhgpunAaOXATd74mV7GD0vL3N3KfB04tU1Zd0faVSiCZlSCpWRcf0aquuMTllTUmyn/UqIztMz/yk+h1GqK8tsP/MkXF7e6Dw9bvhBXnCilEGm+SLZF7IdT7FZriRSV7c7bOMar4t1Ks4HLvrYRm8uJewu8urXGkG6VnKUNxG6+nVBo1NGvREXnfy3XhKCPYMlGRNlTv71iptmtVhtpwCvSp6uJFvmq0a2bD/Nphvf2uFa9C463Dxdrkqi8idUbnmSLaOnS4kWyIee20zHLR+T6VYd96yLhPYYC9xrX241mbCmFnJ06uoaqtzTfpainz4tiGY0OtZO5Y5W5T3Fl5tg+fjYTvVdPg1o9XTD5KbHhNk2imTOJuM6CVGWOe5K+6UsshKunUDlHZkqdqJUAnITmryJT97X6TnpbI/fnm+9hxo9REP/hg6n4NwN7o7JVJ7tVfbRJSHEzZHTl8JOKYUpy3JVQmU7FXh1QpWbbGWn22qyikvTabZTgh6Oo1fGPMmW7WFLwHJPIxb3lg9KKcjJwWoyobKzUdnZWLOyUabLz7OzUdmmPK8v9zNd7nf5ufnSJZJ/+jnf9g21amHNysKaWoKn/fT6fKf4NC9PlKc7yssDs4cRi4cRs4crJncDJnc92e4uZLppZBp1ZLhBpmYucMToRolSUW8dUFI0NHtCZHQxFpgo5b6+VkJVqHYXN1x1rjdMlq5V/L1q0CoZTRGioks+A5eOQrW64FuzVHYhpy+rOLPJ4jByZStozz96lfcUYXa6Gav1Jk4NurvYkqd8I1eOCZXRXYfRReHqYsGgWSDHdFVylHYlOUrKxhp/+Xl2NtmmbDLzJEf2ftnZWE1Xnl9JsC4/z5OEldRVfAXJOX06X5vydMfq6Y7V0w2zhxGzuys5Hgay3V3IdtOT5a4n0wgZRo10I6QZraQaLKQarSQZckjTmci2mi4nSilkmY9dO1HKuvxILPn3dq1EKfe1u9694PbLSVBJJ0plSYq/q57400c5f+IgARFNCKpV19nhiNKy81tY+iwoK2g66DcNWg9xWjiSlJWCxJhTXDx4iupNwvBveHOXUVstVlt91VX1Vlnp+U8H5i1uN+fcxKlBPRiNYDQoXF2sGPUWXPVmDFoOruTgSjauKgsXcyau5gwM5nT0pnS0rCxU8vWTowyTiYwSuhKvJFhdXbAY9FgNeiwGPWaDDouLRo5Bh9lFI8cFclw0THqFyQVMekW2XmE1m+j6Zzp50warBh8N1BFXTSPDCOlGyDKC0nKAHCClaMFlX35cR95E6eqRpMIkSjcaXSrPiVJZy0lqi/vhJwlwPcp5U11yGrd1dkiiIErZPmCVAgr7XDm071r+GS0OTiFIU1iUxu7GY2jV6/8KXkdZHbdnf331MorQ1zGefM8L3A7XWXat7dxgu4Xqe714Cht7/mNw030LE7s5G+L25vnbscLSMVC3R6mNmN1IuTh9OXPmTP79738TFxdHy5YtmTFjBu3aXfuGjAsWLOD111/n+PHj1K9fnw8++IA+ffoUal+lffpy+7QlbD3oYcu4lZX2TTJo+2x/26nBTFsylZmWQ3ZKFhnJWWSnZJGZmk1WqsmWUGWYyc6wkJVlJTsbcm7iwi1NWTFYMzFYMjHkpOOSk47BlIpLdiqGnHTbw5yBISfNttycgSEnHX0ZXi1m1euwGPRYDDosBh1mFx05Bs2eEJlcNHIuJ0TZekW23kq2XpGlt5Klt5Clt5Kj53JfLidRYLK3addcZtZjq9Avptv2WHlyhRW9AosGn/fWsa6lrsBE6Xo1S7ZRJDdcdW4YXVwvPzfaf7rqbQmRUe+GQWfEVWd77ap3Q6+5oNDsI5xWpbAq20/b/0WOrx1+kvva1ie3/cq6V71GYbVeWfe6277GvqyXY7qy7fyvrZf/S7Ja82zfvj+F1WpFs1rQrGZQZrBa0KwWUJfbsNp+Wi1oymzrq8y2/3CtZnQqT//Lj7xtOizorGY0ZbUvM5tzCEjcxV36P9FpYFWwynILl/yaYtCBzhah/acGaFgv/8zTrhQ67cpzjdyHY3+H5ypvu+PDvh3N9lqnrt6/7Xepy32uyBNjnsflD9F8+7i8bVQB7fne8+U2dY0Yb9R2nT7X2v7VfXRleFsQUYkNXQaRXUp0k4XNPZyelP3www8MGTKE2bNn0759e6ZOncqCBQuIiYkhMDAwX//NmzfTtWtXJk2axF133cW8efP44IMP2LlzJ82aNbvh/kozKUuMOcW8j2JsCVkupXCxZGLRu6Fu4p47Ljm2ESkXezKVjiEnw/7csd320FuyuFHKoTQwX06KzC65yZDjCFG2i7qcEOUmRdY8ic6VpMfkQgEJkpYnQbpqmR5UCd8kVYcBneaCHgM6DGi4XG4zoCkX+2sNF7j8GmVbhsNDj1IGlNKjrC4oq63NarW1m6zpZPl/TbVURXCiIs5f45K3hnb6ZTRr9StfyLiS2Fgvf1lzeH0dGlZcsKLDigsW9FjQX27Lfa7XbMt0V7W75Fmux3LVdqyXHxbba81qX+fq7djbNcd1C+xbArHoNHXt94rVdrpbCCfIVra/TAVYr0pFrQWkzNarl6kr6eXV/a+9ft596a69vnLcDnnWu976uel+vlhz+6u823Hse/33cWV/+bed+7u41vpX9llwbAX/jgGs6ur3fK2+tv34ks4Hhi8uf1GyMSsdF4dvL/FT1hUmKWvfvj233HILn3zyCQBWq5WwsDBGjx7NSy+9lK//Aw88QHp6OsuWLbO3dejQgVatWjF79uwb7q80k7IjCzezatX1p3LRWbLzjFDlJk/p6CwZaNZ0NGs6qAyspKNIx0w6Zl0GOS7WK4lP3uRGr+VLdEwuYDLkJkhavmV517fouKnRIgC9VUOndOiVdvlR8HOXAl9ruFivvHaxr6dhsPfDYbmLwuGnwb6OwjZOlfvPOc83/8tv8cp/i1dea9d5TZ7XebfrRjYXfY/yVo1qWDUNnVK8ceES4ck1sWh6ST6cxKK5oDQ91ssPhQ6luWDVdJfbXUDT2ZbpXFCabbnS9LaHTn/VcxfQ9OiyEwlJ3JFvf6dr3IrZIxi0y38lWt6/ltzXVz5gbF/MLn8waJf7OayjA+3yX52WZzvoLreR57ntS15uX6Xy7E/jyva4anuahlK6PPslz/byfqA6xqAgT5/L+1K29S5/37A9VwW8L3vfPB+qV8cMoNkSjNzf49Xr2JZfjkGDAscQHX6feZKR3P3Yt+n4u8tdR0s9x7gD96C/6oN6StMfseadZumqT86rP0iv/mi9+pM2f/+8y66/7tXy7es6277R9m+07tU98m27CPu60f7y3RC5qL/z62wb4HxaNmHHf+Q9l69w0ayYlY5XzE9w9/+9TMe61SlJFaLQ32QysWPHDl5++WV7m06no2fPnmzZsqXAdbZs2cK4ceMc2qKjo1m0aFGB/bOzs8nOcxVcSkoR63qKINknGZThqpEyKzHeMznrH0+GawZZrmaHBMnswuX/+K5HAxyvNjQohVEpXJW66jn252552h2W5SgMOY7LbA+uem3rY7i8rMD9XY7OaTQnBpAGt2ZmccrgQliOmWCLBfT/lMGONdC5XH7oLz9sycOVdt2V51qePro8fTTdVW36q7ZR0Lb1V22jgG3fcH9FiftabQXFobvqX0kJSj6D+rgZGldqNa2ajlqPfuG02hNRWhrz108Tab33TfsH9c4WE3hxUA9nByZK2LnkTG59/yK/Z7egti6e49Ygzms1GFvDeTcAd2pSduHCBSwWC0FBQQ7tQUFBHDp0qMB14uLiCuwfFxdXYP9Jkybx5ptvlkzAN3DEGkOccR/B2Q/aPiSUhTjjfDLrHqGRcsOgXHHFFRc0XC0aBrPtRJoBDVelw1UDF2VrM2o6DErDoGm4osNV02NQtnYXTUOn2caE0DRb8bWm2fITTQOdhqbp7G263OWa7vKXcw0N23Pt8vpanuWafZs6NE1Dp3F5WW6/K6/tGVHuc3uCqV2njRuvk295IfZD7o/CxlHYbV71Ew2yklFr3yLYYrElY9i+dWu9PwSvgGImFYVIZC4nH6KM+dZE6z8NtXQMmrKgND26flMlIauk2g0aQ3z7flw4cYgaEY1oJ1dfVkohvu5Muqc5r/y8nzhrdfSaxnv3NCPE191pMVX6qy9ffvllh5G1lJQUwsJKZ2LZppEdea/1l4RciiH8YnVOVr/IuWpJfN/5K5rVa18q+xTOo3nWcPiQ1vpNdeql1KKUtR6CVrcHXDqGVq2OJGSVXFCtunIrjCrggVvC6doggOMXMqhdw8OpCRk4OSmrUaMGer2e+Ph4h/b4+HiCrzHRb3BwcJH6G41GjCU4X971NKvXniHbu/BttT84Uz0ZnVIMcesiCVllJR/SVY9vTTnOQlQyIb7uTk/Gcjn1PIirqytt2rRh7dq19jar1cratWvp2LFjget07NjRoT/A6tWrr9m/rD334Cy+7/wVr4cO5/vOX/Hcg7OcHZIoTb41bZdOywe1EEKIm+T005fjxo1j6NChtG3blnbt2jF16lTS09N5/PHHARgyZAg1a9Zk0qRJADz77LN069aNKVOm0LdvX+bPn8/27dv5/PPPnfk2HDSr115Gx4QQQghRJE5Pyh544AHOnz/PG2+8QVxcHK1atWLlypX2Yv6TJ0+iy1PY3KlTJ+bNm8drr73GK6+8Qv369Vm0aFGh7lEmhBBCCFFeOf0+ZWVNJiQXQgghRFkqbO4h19YLIYQQQpQDkpQJIYQQQpQDkpQJIYQQQpQDkpQJIYQQQpQDkpQJIYQQQpQDkpQJIYQQQpQDTr9PWVnLvQNISkqKkyMRQgghRFWQm3Pc6C5kVS4pS01NBSi1ScmFEEIIIQqSmpqKr6/vNZdXuZvHWq1Wzp49i7e3N5qmlfj2U1JSCAsL49SpU3JzWieTY1F+yLEoP+RYlA9yHMqPsjgWSilSU1MJDQ11mKXoalVupEyn01GrVq1S34+Pj4/8Qysn5FiUH3Isyg85FuWDHIfyo7SPxfVGyHJJob8QQgghRDkgSZkQQgghRDkgSVkJMxqNTJgwAaPR6OxQqjw5FuWHHIvyQ45F+SDHofwoT8eiyhX6CyGEEEKURzJSJoQQQghRDkhSJoQQQghRDkhSJoQQQghRDkhSJoQQQghRDkhSVgwzZ86kdu3auLm50b59e/7666/r9l+wYAGNGjXCzc2N5s2b88svv5RRpJVfUY7FF198QZcuXfD398ff35+ePXve8NiJwivqv4tc8+fPR9M0Bg4cWLoBViFFPRZJSUmMHDmSkJAQjEYjDRo0kP+nSkBRj8PUqVNp2LAh7u7uhIWFMXbsWLKyssoo2srr999/p1+/foSGhqJpGosWLbrhOuvXr6d169YYjUbq1avH3LlzSz1OAJQokvnz5ytXV1f19ddfqwMHDqjhw4crPz8/FR8fX2D/TZs2Kb1erz788EN18OBB9dprrymDwaD27dtXxpFXPkU9Fg8//LCaOXOm2rVrl/r777/VY489pnx9fdXp06fLOPLKp6jHIldsbKyqWbOm6tKlixowYEDZBFvJFfVYZGdnq7Zt26o+ffqojRs3qtjYWLV+/Xq1e/fuMo68cinqcfj++++V0WhU33//vYqNjVWrVq1SISEhauzYsWUceeXzyy+/qFdffVX9/PPPClALFy68bv9jx44pDw8PNW7cOHXw4EE1Y8YMpdfr1cqVK0s9VknKiqhdu3Zq5MiR9tcWi0WFhoaqSZMmFdj//vvvV3379nVoa9++vXrqqadKNc6qoKjH4mpms1l5e3urb775prRCrDKKcyzMZrPq1KmT+vLLL9XQoUMlKSshRT0Ws2bNUnXq1FEmk6msQqwSinocRo4cqW6//XaHtnHjxqlbb721VOOsagqTlL3wwguqadOmDm0PPPCAio6OLsXIbOT0ZRGYTCZ27NhBz5497W06nY6ePXuyZcuWAtfZsmWLQ3+A6Ojoa/YXhVOcY3G1jIwMcnJyqFatWmmFWSUU91i89dZbBAYG8sQTT5RFmFVCcY7FkiVL6NixIyNHjiQoKIhmzZrx3nvvYbFYyirsSqc4x6FTp07s2LHDforz2LFj/PLLL/Tp06dMYhZXOPNzu8pNSH4zLly4gMViISgoyKE9KCiIQ4cOFbhOXFxcgf3j4uJKLc6qoDjH4movvvgioaGh+f7xiaIpzrHYuHEjX331Fbt37y6DCKuO4hyLY8eO8dtvvzF48GB++eUXjhw5wogRI8jJyWHChAllEXalU5zj8PDDD3PhwgU6d+6MUgqz2cy//vUvXnnllbIIWeRxrc/tlJQUMjMzcXd3L7V9y0iZqJLef/995s+fz8KFC3Fzc3N2OFVKamoqjz76KF988QU1atRwdjhVntVqJTAwkM8//5w2bdrwwAMP8OqrrzJ79mxnh1alrF+/nvfee49PP/2UnTt38vPPP7N8+XLefvttZ4cmypCMlBVBjRo10Ov1xMfHO7THx8cTHBxc4DrBwcFF6i8KpzjHItfkyZN5//33WbNmDS1atCjNMKuEoh6Lo0ePcvz4cfr162dvs1qtALi4uBATE0PdunVLN+hKqjj/LkJCQjAYDOj1entb48aNiYuLw2Qy4erqWqoxV0bFOQ6vv/46jz76KMOGDQOgefPmpKen8+STT/Lqq6+i08kYSlm51ue2j49PqY6SgYyUFYmrqytt2rRh7dq19jar1cratWvp2LFjget07NjRoT/A6tWrr9lfFE5xjgXAhx9+yNtvv83KlStp27ZtWYRa6RX1WDRq1Ih9+/axe/du+6N///7cdttt7N69m7CwsLIMv1Ipzr+LW2+9lSNHjtgTY4B//vmHkJAQSciKqTjHISMjI1/ilZsoK5miukw59XO71C8lqGTmz5+vjEajmjt3rjp48KB68sknlZ+fn4qLi1NKKfXoo4+ql156yd5/06ZNysXFRU2ePFn9/fffasKECXJLjBJS1GPx/vvvK1dXV/Xjjz+qc+fO2R+pqanOeguVRlGPxdXk6suSU9RjcfLkSeXt7a1GjRqlYmJi1LJly1RgYKB65513nPUWKoWiHocJEyYob29v9d///lcdO3ZM/frrr6pu3brq/vvvd9ZbqDRSU1PVrl271K5duxSgPvroI7Vr1y514sQJpZRSL730knr00Uft/XNvifH888+rv//+W82cOVNuiVGezZgxQ4WHhytXV1fVrl079eeff9qXdevWTQ0dOtSh///+9z/VoEED5erqqpo2baqWL19exhFXXkU5FhEREQrI95gwYULZB14JFfXfRV6SlJWsoh6LzZs3q/bt2yuj0ajq1Kmj3n33XWU2m8s46sqnKMchJydHTZw4UdWtW1e5ubmpsLAwNWLECJWYmFj2gVcy69atK/D//tzf/9ChQ1W3bt3yrdOqVSvl6uqq6tSpo+bMmVMmsWpKybioEEIIIYSzSU2ZEEIIIUQ5IEmZEEIIIUQ5IEmZEEIIIUQ5IEmZEEIIIUQ5IEmZEEIIIUQ5IEmZEEIIIUQ5IEmZEEIIIUQ5IEmZEEIIIaq033//nX79+hEaGoqmaSxatKjI21BKMXnyZBo0aIDRaKRmzZq8++67RdqGJGVCiFLTvXt3xowZ4+wwAFi/fj2appGUlFTi2544cSJBQUHF/s+8uGrXrs3UqVPLbH9CVFbp6em0bNmSmTNnFnsbzz77LF9++SWTJ0/m0KFDLFmyhHbt2hVpGy7F3rsQQpRT3bt3p1WrVmWSsPz999+8+eabLFy4kA4dOuDv71/q+8y1bds2PD09y2x/QlRWvXv3pnfv3tdcnp2dzauvvsp///tfkpKSaNasGR988AHdu3cHbP8PzJo1i/3799OwYUMAIiMjixyHjJQJIcRNOHr0KAADBgwgODgYo9FY6vs0mUwABAQE4OHhUer7E6KqGzVqFFu2bGH+/Pns3buX++67j169enH48GEAli5dSp06dVi2bBmRkZHUrl2bYcOGcenSpSLtR5IyIUSZyc7OZvz48dSsWRNPT0/at2/P+vXr7cvnzp2Ln58fq1atonHjxnh5edGrVy/OnTtn72M2m3nmmWfw8/OjevXqvPjiiwwdOpSBAwcC8Nhjj7FhwwamTZuGpmlomsbx48ft6+/YsYO2bdvi4eFBp06diImJuW7M+/bt4/bbb8fd3Z3q1avz5JNPkpaWBthOW/br1w8AnU6HpmkFbiMxMZHBgwcTEBCAu7s79evXZ86cOfblp06d4v7778fPz49q1aoxYMAAh5gfe+wxBg4cyLvvvktoaKj9m/jVpy+TkpIYNmwYAQEB+Pj4cPvtt7Nnzx778j179nDbbbfh7e2Nj48Pbdq0Yfv27dd9/0JUdSdPnmTOnDksWLCALl26ULduXcaPH0/nzp3t/46PHTvGiRMnWLBgAd9++y1z585lx44d3HvvvUXalyRlQogyc6NvmwAZGRlMnjyZ7777jt9//52TJ08yfvx4+/IPPviA77//njlz5rBp0yZSUlIc6rimTZtGx44dGT58OOfOnePcuXOEhYXZl7/66qtMmTKF7du34+Liwv/93/9dM9709HSio6Px9/dn27ZtLFiwgDVr1jBq1CgAxo8fb/9POXdfBXn99dc5ePAgK1assJ/mqFGjBgA5OTlER0fj7e3NH3/8waZNm+zJaO6IGMDatWuJiYlh9erVLFu2rMD93HfffSQkJLBixQp27NhB69at6dGjh/3b+uDBg6lVqxbbtm1jx44dvPTSSxgMhmu+fyGE7YuZxWKhQYMGeHl52R8bNmywj5RbrVays7P59ttv6dKlC927d+err75i3bp1N/zi50AJIUQp6datm3r22WeVUkqdOHFC6fV6debMGYc+PXr0UC+//LJSSqk5c+YoQB05csS+fObMmSooKMj+OigoSP373/+2vzabzSo8PFwNGDCgwP3mWrdunQLUmjVr7G3Lly9XgMrMzCww/s8//1z5+/urtLQ0h3V0Op2Ki4tTSim1cOFCdaP/Svv166cef/zxApd99913qmHDhspqtdrbsrOzlbu7u1q1apVSSqmhQ4eqoKAglZ2d7bBuRESE+vjjj5VSSv3xxx/Kx8dHZWVlOfSpW7eu+uyzz5RSSnl7e6u5c+deN1YhqjpALVy40P56/vz5Sq/Xq0OHDqnDhw87PM6dO6eUUuqNN95QLi4uDtvJyMhQgPr1118LvW8p9BdClIm83zbzys7Opnr16vbXHh4e1K1b1/46JCS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      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "results.plot(ax=ax, marker='.')\n",
    "ax.set_xlabel('length of series')\n",
    "ax.set_ylabel('time (seconds)')\n",
    "ax.set_title('Profiling create_train_X_y()')\n",
    "ax.legend(title='number of lags');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5ef6f660",
   "metadata": {},
   "source": [
    "## Profiling predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f484dc32",
   "metadata": {},
   "outputs": [],
   "source": [
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = DummyRegressor(),\n",
    "                 lags      = 24\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dbc921c4-ab89-4d16-8ddc-1b88fd336c32",
   "metadata": {},
   "outputs": [
    {
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       "/   _/                      v5.1.1\n",
       "\n",
       "Cell [11]\n",
       "\n",
       "0.080 <module>  /tmp/ipykernel_26803/3639615597.py:1\n",
       "|- 0.053 [self]  /tmp/ipykernel_26803/3639615597.py\n",
       "`- 0.027 ForecasterRecursive.predict  skforecast/recursive/_forecaster_recursive.py:1490\n",
       "      [4 frames hidden]  skforecast, <built-in>\n",
       "         0.026 ForecasterRecursive._recursive_predict  skforecast/recursive/_forecaster_recursive.py:1287\n",
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    "%%pyinstrument\n",
    "\n",
    "_ = forecaster.predict(steps=1000)"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11b048b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = HistGradientBoostingRegressor(max_iter=10, random_state=123),\n",
    "                 lags      = 24\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data)"
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  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7cdb7ac",
   "metadata": {},
   "outputs": [
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       "0.928 <module>  /tmp/ipykernel_26803/3639615597.py:1\n",
       "`- 0.928 ForecasterRecursive.predict  skforecast/recursive/_forecaster_recursive.py:1490\n",
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